Industrial internet of things for inspection data processing, control method, and storage medium thereof

ABSTRACT

The embodiment of the present disclosure provides an Industrial Internet of Things for inspection data processing, comprising a management platform. The management platform is configured to perform operations including: determining an inspection task, the inspection task including detecting at least one detection site; sending instructions to an inspection robot based on the inspection task to move the inspection robot to a target position to be inspected; obtaining detection data based on the inspection robot, and determining subsequent detection or processing operations based on the detection data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.202210651128.9, filed on Jun. 10, 2022, the contents of which are herebyincorporated by reference to its entirety.

TECHNICAL FIELD

The present disclosure generally relates to intelligent manufacturingtechnology, in particular, to Industrial Internet of Things forinspection data processing, control method, and storage medium thereof.

BACKGROUND

In some special workshops and plants, such as hazardous chemicalsprocessing workshop, flammable and explosive materials manufacturingworkshop, nuclear power plant equipment room and other special areas,the special environment makes the area very prone to accidents due toleakage, explosion, diffusion, and so on. Therefore, robots are usuallyused to perform corresponding operations in high-risk areas of suchhigh-risk industries. The most common robot is the inspection robot.

The inspection robot carries out inspection through the inspectioncommand issued in advance and the specified path. During inspection, itwill pass through several detection sites. The inspection robot performscorresponding data monitoring at each detection site based on theinspection command, such as environmental or equipment temperaturemonitoring, gas concentration monitoring, dust concentration monitoring,radiation monitoring, etc., and sends the detection data to relevantsystems or departments for circular data monitoring, so that manualdetection is not required, and casualties caused by accidents can beavoided.

In the prior art, the inspection robot may need to set up multipleinspection electrical components or multiple inspection robots toconduct inspection according to the interval time during inspection,which leads to a large number of different types of data interactionduring inspection and may also receive commands and send commandexecution results at any time. Therefore, both the inspection robot andits supporting data processing system need a large amount of dataprocessing and storage, and all kinds of data in the existing technologyneed to be classified, which makes the data processing more cumbersome,affects the subsequent data analysis and call, and affects thesubsequent processing flow and time. As a result, the existinginspection robot and its supporting data processing system are not onlycomplex and difficult to build, but also expensive, which are unable tobe better popularized and promoted.

SUMMARY

One of the embodiments of the present disclosure provides an IndustrialInternet of Things for inspection data processing, comprising amanagement platform. The management platform is configured to performoperations including: determining an inspection task, the inspectiontask including detecting at least one detection site; sendinginstructions to an inspection robot based on the inspection task to movethe inspection robot to a target position to be inspected; obtainingdetection data based on the inspection robot, and determining subsequentdetection or processing operations based on the detection data.

One of the embodiments of the present disclosure provides an IndustrialInternet of Things for inspection data processing. The IndustrialInternet of Things further comprises a user platform, a serviceplatform, a sensor network platform, and an object platform; the userplatform, the service platform, the management platform, the sensornetwork platform, and the object platform are interacted sequentiallyfrom top to bottom. The service platform and the sensor network platformadopt independent layout, and the management platform adopts front subplatform layout. The independent layout means that the service platformor the sensor network platform is provided with a plurality ofindependent sub platforms, the plurality of independent sub platformsrespectively store, process, and/or transmit data of different lowerplatforms. The front sub platform layout means that the managementplatform is provided with a general platform and a plurality of subplatforms, the plurality of sub platforms respectively store and processdata of different types or different receiving objects sent by thedifferent lower platforms, and the general platform stores and processesdata of the plurality of sub platforms after summarizing, and transmitsthe data of the plurality of sub platforms to upper platforms. Theobject platform is configured as the inspection robot in an intelligentproduction line. The control method includes: when the inspection robotinspects the detection site, obtaining detection data of equipment orenvironment corresponding to the detection site, associatingidentification information of the detection site with the detectiondata, packing the associated detection data into a detection package,and sending the detection package to a sub platform of the sensornetwork platform corresponding to the detection site; and receiving thedetection packet, converting the detection packet into a data filerecognized by the management platform, and sending the data file to asub platform of the management platform corresponding to the detectionsite by the sub platform of the sensor network platform; receiving thedata file, extracting the detection data in the data file forcomparison, obtaining a comparison result, storing identificationinformation of the detection site, the detection data, and thecomparison result, and uploading them to the general platform of themanagement platform by the sub platform of the management platform;after receiving corresponding data, based on the comparison result, thegeneral platform of the management platform executes: sending feedbackinstructions to corresponding sub platform of the management platform bythe general platform of the management platform, sending the feedbackinstructions to the inspection robot through corresponding sub platformof the sensor network platform by the corresponding sub platform of themanagement platform, and continuing to perform detection after theinspection robot receiving the feedback instructions; or the generalplatform of the management platform executes: sending the identificationinformation of the detection site, the detection data, and thecomparison result to corresponding sub platform of the service platformby the general platform of the management platform; receiving thecorresponding data and sending the corresponding data to the userplatform by the corresponding sub platform of the service platform, andsending processing instructions based on the corresponding data to thecorresponding sub platform of the service platform, the general platformof the management platform, corresponding sub platform of the managementplatform and corresponding sub platform of the sensor network platformby the user platform; and receiving the processing instructions andconverting the processing instructions into an instruction filerecognized by the inspection robot by the corresponding sub platform ofthe sensor network platform, and receiving the instruction file andperforming corresponding processing operations by the inspection robot.

One of the embodiments of the present disclosure provides non-transitorycomputer-readable storage medium, which stores computer instructions,when the computer reads the computer instructions in the storage medium,the computer runs the control method of an Industrial Internet of Thingsfor inspection data processing described in any one of the above items.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be further described in the form ofexemplary embodiments, which will be described in detail by theaccompanying drawings. These embodiments are not restrictive. In theseembodiments, the same number represents the same structure, wherein:

FIG. 1 shows an exemplary flowchart of an Industrial Internet of Thingsfor inspection data processing according to some embodiments of thepresent disclosure;

FIG. 2 shows an exemplary structural frame diagram of an IndustrialInternet of Things for inspection data processing according to someembodiments of the present disclosure;

FIG. 3 shows an exemplary flowchart of a control method of an IndustrialInternet of Things for inspection data processing according to someembodiments of the present specification;

FIG. 4 shows an exemplary schematic diagram of determining a targetposition to be inspected based on a reinforcement learning modelaccording to some embodiments of the present disclosure;

FIG. 5 shows an exemplary flowchart for determining an abnormalprobability of the target position to be inspected based on a frequentitem according to some embodiments of the present disclosure;

FIG. 6 shows an exemplary schematic diagram of determining the abnormalprobability of the target position to be inspected based on the abnormalprobability determination model according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In order to more clearly explain the technical scheme of the embodimentsof the present disclosure, the following will briefly introduce thedrawings that need to be used in the description of the embodiments.Obviously, the drawings in the following description are only someexamples or embodiments of the present disclosure. For those skilled inthe art, the present disclosure can also be applied to other similarscenarios according to these drawings without creative work. Unless itis obvious from the language environment or otherwise stated, the samelabel in the figure represents the same structure or operation.

It should be understood that the “system”, “device”, “unit” and/or“module” used herein is a method for distinguishing differentcomponents, elements, components, parts or assemblies at differentlevels. However, if other words serve the same purpose, they may bereplaced by other expressions.

As shown in the description and claims, the words “one”, and/or “this”do not specifically refer to the singular, but may also include theplural, unless the context clearly indicates exceptions. Generallyspeaking, the terms “include” and “comprise” only indicate that thesteps and elements that have been clearly identified are included, andthese steps and elements do not constitute an exclusive list. Methods orequipment may also contain other steps or elements.

A flowchart is used in the present disclosure to explain the operationperformed by the system according to the embodiment of the presentdisclosure. It should be understood that the preceding or subsequentoperations are not necessarily performed accurately in sequence.Instead, you can process the steps in reverse order or simultaneously.At the same time, you can add other operations to these procedures, orremove one or more operations from these procedures.

The application scenarios of the Industrial Internet of Things (loT) forinspection data processing may include processing devices, networks,storage devices, and inspection robots. The processing device mayprocess information and/or data related to the application scenario ofthe Industrial Internet of Things for inspection data processing, andthe management platform may be implemented in the processing device. Thenetwork may realize the communication of various components in theapplication scenario. A storage device may store data, instructions,and/or any other information. The inspection robot may receive theinstructions from the management platform and perform the inspectiontask based on the instructions to inspect the industrial productionenvironment or equipment.

FIG. 1 shows an exemplary flowchart of an Industrial Internet of Thingsfor inspection data processing according to some embodiments of thepresent disclosure. In some embodiments, the process 100 may be executedby the management platform. As shown in FIG. 1 , the process 100includes the following steps.

In step 110, determining an inspection task.

The inspection task refers to a task of detecting at least one detectionsite and obtaining detection data. The at least one detection site mayrefer to sites in the inspection task where inspection is required andthe detection data is obtained. The at least one detection site may haveidentification information. For example, the identification informationof a detection site may include number information of the detection site(e.g., a No. 1 detection site, a No. 2 detection site) and positioninformation of the detection site (e.g., three-dimensional coordinates).In some embodiments, the inspection of the at least one detection sitemay be completed by an inspection robot. The detection data may refer todata obtained by detecting equipment or environment corresponding to thedetection site. For example, the detection data of the detection sitemay include temperature, humidity, air pressure, etc. of the environmentcorresponding to the detection site, and operation statuses (normal orabnormal) of the equipment corresponding to the detection site.

In some embodiments, the inspection task may be determined by obtainingan input from a manager. For example, contents to be detected (detectionsites, required detection data, etc.) may be determined and uploaded tothe management platform by the manager, and the management platform maygenerate corresponding inspection tasks.

In step 120, sending instructions to an inspection robot based on theinspection task to move the inspection robot to a target position to beinspected.

Positions to be inspected may refer to detection positions that have notbeen detected in this inspection task. For example, the inspection taskmay be a task that detects the No. 1 detection site, the No. 2 detectionsite, and a No. 3 detection site and obtains the detection data. Beforethe management platform sends instructions to the inspection robot, theNo. 1 detection site, the No. 2 detection site and the No. 3 detectionsite may be the positions to be inspected. The target position to beinspected may refer to a position to be inspected which the inspectionrobot will go.

The instructions may be used to control the inspection robot to go tothe target position to be inspected for detection. For example, contentsof the instructions may include number information and positioninformation of the detection site corresponding to the target positionto be inspected which the inspection robot is required to go.

In some embodiments, the management platform may determine the targetposition to be inspected based on the number information of thedetection site in the inspection task, thereby determining theinstructions. For example, the management platform may determine adetection site with lowest number corresponding to number information inthe inspection task as the target position to be inspected. For example,the inspection task may be to detect the No. 1 detection site, the No. 2detection site, and the No. 3 detection site and obtain the detectiondata. The management platform may determine the No. 1 detection positionas the target position to be inspected and send instructions to theinspection robot to go to the target position to be inspected forinspection.

In some embodiments, when the management platform sends the instructionsto the inspection robot to move the inspection robot to a position to beinspected and obtain detection data of the detection site correspondingto the position to be inspected, the management platform may remove thedetection site from the position to be inspected of the inspection taskand mark it as an inspected position.

In some embodiments, the management platform may determine the targetposition to be inspected based on a current position of the inspectionrobot, thereby determining the instructions.

The current position of the inspection robot may be expressed in variousways, such as three-dimensional coordinates. The management platform mayobtain a current position of the inspection robot through one or moreposition sensors deployed on the inspection robot or other methods.

In some embodiments, the management platform may determine the targetposition to be inspected based on distances between the current positionof the inspection robot and at least one position to be inspected. Forexample, the current position of the inspection robot may be (0, 0, 0).The positions to be inspected in the inspection task may include the No.1 detection position, the No. 2 detection position, and the No. 3detection position. The three positions to be inspected may be locatedat (5, 0, 0), (10, 0, 0), (3, 0, 0). Through calculation, it may beconcluded that the No. 3 detection position is the closest to thecurrent position of the inspection robot, so the No. 3 detectionposition is determined as the target position to be inspected.

In some embodiments of the present disclosure, based on the currentposition of the inspection robot, the target position to be inspectedmay be determined by the positions to be inspected closest to thecurrent position, which can effectively reduce the length of theinspection robot's moving path, reduce the situation such as detour, andimprove the inspection efficiency.

In some embodiments, the management platform may determine the targetposition to be inspected based on a reinforcement learning model. In thereinforcement learning model, a reward value of the inspection robotperforming an action in the reinforcement learning model may be relatedto a distance between the current position of the inspection robot andthe target position to be inspected. For more information about thereinforcement learning model, determining the target position to beinspected based on the reinforcement learning model, and the rewardvalue, please refer to FIG. 4 and its related descriptions.

In step 130, obtaining detection data based on the inspection robot, anddetermining subsequent detection or processing operations based on thedetection data.

In some embodiments, the management platform may obtain the detectiondata based on the inspection robot and send detection data of thedetection site to an internal research and judgment system. The internalresearch and judgment system may be an internal system of the managementplatform for research and judgment of the detection data. The internalresearch and judgment system may automatically research and judge thedetection data based on preset rules to determine the subsequentdetection or processing operations. For example, the internal researchand judgment system may judge that the detection data is normal (forexample, it meets standards in the preset rules) and upload research andjudgment results to the management platform. The management platform maysend instructions to the inspection robot based on the research andjudgment results, so that the inspection robot may move to the positionsto be inspected in the inspection task to perform a next detection. Themanagement platform may determine instructions based on the above mannerin step 120.

In some embodiments, if there is no position to be inspected in currentinspection task, the inspection robot may send receipt information oftask completion to the management platform and return to a startingposition (or stand by in place), waiting for a next inspection task.

In some embodiments, the management platform may determine a comparisonresult based on the detection data of the detection site and a detectiondata comparison table, and determine subsequent operations based on thecomparison result. For more information about the detection datacomparison table and the comparison result, please refer to FIG. 3 andits related descriptions.

As shown in FIG. 2 , the first embodiment of the present disclosure aimsto provide an Industrial Internet of Things for inspection dataprocessing. The Industrial Internet of Things for inspection dataprocessing includes a user platform, a service platform, a managementplatform, a sensor network platform, and an object platform which areinteracted sequentially from top to bottom.

The service platform and the sensor network platform may adoptindependent layout, and the management platform may adopt front subplatform layout. The independent layout may mean that the serviceplatform or the sensor network platform is provided with a plurality ofindependent sub platforms, the plurality of independent sub platformsrespectively store, process, and/or transmit data of different lowerplatforms. The front sub platform layout may mean that the managementplatform is provided with a general platform and a plurality of subplatforms. The plurality of sub platforms may respectively store andprocess the data of different types or different receiving objects sentby the different lower platforms. The general platform may store andprocess data of the plurality of sub platforms after summarizing andtransmit the data of the plurality of sub platforms to upper platforms.The object platform may be configured as the inspection robot in anintelligent production line.

The obtaining detection data based on the inspection robot may comprise:when the inspection robot inspects the detection site, obtainingdetection data of equipment or environment corresponding to thedetection site, associating identification information of the detectionsite with the detection data, packing the associated detection data intoa detection package, and sending the detection package to the subplatform of the sensor network platform corresponding to the detectionsite. The identification information of the detection site may at leastcomprise number information of detection site and number information ofcurrent inspection robot. The sub platform of the sensor networkplatform receives a detection packet, converts the detection packet intoa data file recognized by the management platform, and sends data fileto the sub platform of the management platform corresponding to thedetection site.

The determining subsequent detection or processing operations based onthe detection data may comprise: receiving the data file, extracting thedetection data in the data file for comparison, obtaining a comparisonresult, storing identification information of the detection site, thedetection data, and the comparison result, and uploading them to thegeneral platform of the management platform by the sub platform of themanagement platform.

After receiving corresponding data, based on the comparison result, thegeneral platform of the management platform may execute: sendingfeedback instructions to corresponding sub platform of the managementplatform by the general platform of the management platform, sending thefeedback instructions to the inspection robot through corresponding subplatform of the sensor network platform by the corresponding subplatform of the management platform, and continuing to perform detectionafter the inspection robot receiving the feedback instructions. Afterreceiving corresponding data, based on the comparison result, thegeneral platform of the management platform may execute: sending theidentification information of the detection site, the detection data,and the comparison result to corresponding sub platform of the serviceplatform by the general platform of the management platform; receivingthe corresponding data and sending the corresponding data to the userplatform by the corresponding sub platform of the service platform, andsending processing instructions based on the corresponding data to thecorresponding sub platform of the service platform, the general platformof the management platform, corresponding sub platform of the managementplatform and corresponding sub platform of the sensor network platformby the user platform. The inspection robot may receive the instructionfile and perform corresponding processing operations.

The Industrial Internet of Things for inspection data processing and acontrol method thereof are provided in the present disclosure. TheInternet of Things may be built based on a five-platform structure, inwhich the service platform and the sensor network platform are arrangedindependently, so as to provide an equivalent and independent subplatform of the service platform and an equivalent and independent subplatform of the sensor network platform corresponding to differentdetection sites respectively, and then use the detection sites toclassify the data. It is associated with the detection sites duringsubsequent data processing, storage and call, which facilitates dataprocessing and monitoring, and the data classification is clear withoutworrying about data errors. In addition, the management platform adoptsa front sub platform layout, and uses a general sub platform to connecteach sub platform of the service platform and each sub platform of themanagement platform, as well as the classification and diversion ofdata, which can not only ensure the connection and data connectionbetween the management platform and the upper and lower platforms, butalso further reduce the data processing pressure of the managementplatform. Through various data processing of the general platform andsub platforms of the management platform, the data processing demand ofeach platform of the general platform and sub platforms of themanagement platform is greatly reduced, the construction cost is alsoreduced, and the data processing speed and capacity of each platform arefurther improved.

When using the Industrial Internet of Things for inspection dataprocessing in the present disclosure, in combination with the IndustrialInternet of Things for inspection data processing and the control methodthereof, through the identification information of the current detectionposition of the inspection robot, all data obtained at the detectionsites are independently processed and transmitted according to the subplatform of the corresponding sensor network platform and the managementplatform, and are processed through the general platform of themanagement platform, then the corresponding data processing is carriedout through the sub platform of the corresponding service platform, sothat the overall structure of the Internet of Things is divided intoseveral sub IoT classified according to the detection sites. The databetween each sub IoT does not affect each other, and the data source andprocessing path are clear, which is convenient for subsequent dataprocessing and calling. At the same time, separate data processingthrough each sub IoT can also ensure that each sub IoT can quickly andeffectively perform data related operations without waiting orreclassification. Each sub IoT can also set a physical structureindependently based on preset needs of the corresponding detectionsites, without unnecessary waste, which further reduces the buildingstructure and cost of the Internet of Things.

It should be noted that the user platform in this embodiment may be adesktop computer, tablet computer, notebook computer, mobile phone orother electronic devices that can realize data processing and datacommunication, which is not limited here. In specific applications, thefirst server and the second server may adopt a single server or a servercluster, and there are no too many restrictions here. It should beunderstood that the data processing process mentioned in this embodimentmay be processed by the processor of the server, and the data stored inthe server may be stored on the storage device of the server, such asthe hard disk and other memories. In specific applications, the sensornetwork platform may adopt multiple groups of gateway servers ormultiple groups of intelligent routers, which are not limited here. Itshould be understood that the data processing process mentioned in theembodiment of the present disclosure may be processed by the processorof the gateway server, and the data stored in the gateway server may bestored on the storage device of the gateway server, such as hard disk,SSD, and other memories.

It is further explained that the Industrial Internet of Things forinspection data processing makes use of the unique design of the frontsub platform of the management platform, so that the management platformhas a general platform and several sub platforms. Through the generalplatform of the management platform, the sub platforms of the serviceplatform and the management platform can process, classify and adjustthe data of the upper and lower platforms, thus serving as a connectinglink between the preceding and the following. The corresponding data isconnected with other platforms corresponding to the detection sitethrough the detection site information to ensure that each sub platformcan quickly realize data processing without causing data congestion, andthe data classification is also very clear. The general and sub platformstructure also reduces the data processing pressure of the generalplatform and each sub platform of the management platform, making iteasier to build the Internet of Things and reducing the cost.

In practical application, it may be necessary for the inspection robotto interrupt an established inspection route and rush to a detectionposition to be detected due to temporary adjustment and emergencies.Based on this, when the inspection robot performs inspection and theuser platform sends detection instructions of specified detection site,the general platform of the management platform may obtain latestdetection site information of the inspection robot based on storedidentification information of the detection site, take next twodetection sites of detection site corresponding to the detection siteinformation as target objects, and send the detection instruction ofspecified detection site to a sub platform of the sensor networkplatform corresponding to the target objects. When the inspection robotinspects any one of the target objects, the inspection robot may receivethe detection instruction of specified detection site, interrupt thedetection, move to the specified detection site for detection, andreturn to the interrupted detection site to continue the detection afterthe detection is completed.

By obtaining the detection site information of the inspection robot, thecurrent position of the inspection robot may be obtained, and next twodetection sites corresponding to the current position of the inspectionrobot may be taken as a target object. When the inspection robot movesto any target object, it may receive instructions to performcorresponding detection. In this scheme, the next two detection sites ofminimum detection site of the inspection robot may be taken as thetarget object, so that the inspection robot can avoid leaving thecorresponding detection site when the instructions reach the nextdetection site of the corresponding detection site. It can take thesecond detection site as an instruction receive detection site, so as toavoid a problem that the inspection robot cannot receive theinstructions.

Since the detection instructions of specified detection site will besent to two target objects, if the inspection robot has received theinstructions at a first target object, it is necessary to cancel theinstructions of a second target object to prevent the inspection robotfrom re executing. The instruction cancellation steps may be as follows:when the inspection robot inspects any one of the target objects, theinspection robot receives the detection instruction of specifieddetection site and simultaneously sends instruction receivinginformation to corresponding sub platform of the sensor networkplatform. The sub platform of the sensor network platform sends theinstruction receiving information to the general platform of themanagement platform through corresponding sub platform of the managementplatform. The general platform of the management platform obtains theinstruction receiving information and sends instruction cancellationinformation to a sub platform of the management platform correspondingto another target object. The sub platform of the management platformreceives the instruction cancellation information and sends theinstruction cancellation information to the corresponding sub platformof the sensor network platform, after the corresponding sub platform ofthe sensor network platform receiving the instruction cancellationinformation, the corresponding sub platform of the sensor networkplatform cancels the detection instruction of specified detection site.

In the specific application, the sub platform of the management platformmay receive the data file, extract the detection data in the data filefor comparison, obtain a comparison result, and store identificationinformation of the detection site, the detection data and the comparisonresult and upload them to the general platform of the managementplatform. Specifically, the sub platform of the management platform maypre store a detection data comparison table of corresponding detectionsite; after receiving the data file, the sub platform of the managementplatform may extract the detection data in the data file and compare thedetection data with data in the detection data comparison table; whenthe detection data meets data requirements in the detection datacomparison table, the comparison result may be that the data is normal;when the detection data does not meet the data requirements in thedetection data comparison table, the comparison result may be that thedata is abnormal; after the comparison is completed, the sub platform ofthe management platform may correlate the identification information ofthe detection site, the detection data and the comparison result, storethem and upload them to the general platform of the management platform.

After the comparison is completed, as one of the specific executionsteps, when the comparison result is that the data is normal, thegeneral platform of the management platform may send the feedbackinstructions, and the inspection robot may continue to perform detectionafter receiving the feedback instructions and move to a next detectionsite.

After the comparison is completed, as a second of the specific executionsteps, when the comparison result is that the data is abnormal, thegeneral platform of the management platform may send the identificationinformation of the detection site, the detection data and the comparisonresult to the corresponding sub platform of the service platform.

The corresponding sub platform of the service platform may receive thecorresponding data and send it to the user platform, the user platformmay send processing instructions based on the corresponding data, theprocessing instructions may at least include the detection siteinformation and inspection task adjustment data. Corresponding to thedetection site information, the user platform may send the processinginstructions to the corresponding sub platform of the service platform,the general platform of the management platform, the corresponding subplatform of the management platform and the corresponding sub platformof the sensor network platform. The corresponding sub platform of thesensor network platform may receive the processing instructions andconvert the processing instructions into an instruction file than can berecognized by the inspection robot. The inspection robot may receive theinstruction file, extract the inspection task adjustment data in theinstruction file, and perform inspection based on the inspection taskadjustment data.

In some embodiments, the inspection robot may receive the instructionfile, extract the inspection task adjustment data in the instructionfile, and perform inspection based on the inspection task adjustmentdata. Specifically, the inspection robot may receive the instructionfile and extract the inspection task adjustment data in the instructionfile and take the inspection task adjustment data as update data toupdate original stored inspection data of the inspection robot. Afterthe update, the inspection robot may perform inspection according toupdated inspection data. The inspection data may at least include aninspection route, coordinates of the detection sites and detection itemsof the detection sites.

As shown in FIG. 3 , a second embodiment of the present disclosureprovides a control method for the Industrial Internet of Things forinspection data processing. The Industrial Internet of Things forinspection data processing comprises a user platform, a serviceplatform, a management platform, a sensor network platform, and anobject platform which are interacted sequentially from top to bottom.The service platform and the sensor network platform may adoptindependent layout, and the management platform may adopt front subplatform layout. The independent layout may mean that the serviceplatform or the sensor network platform is provided with a plurality ofindependent sub platforms, the plurality of independent sub platformsrespectively store, process, and/or transmit data of different lowerplatforms. The front sub platform layout may mean that the managementplatform is provided with a general platform and a plurality of subplatforms. The plurality of sub platforms may respectively store andprocess the data of different types or different receiving objects sentby the different lower platforms. A general platform may store andprocess data of the plurality of sub platforms after summarizing andtransmit the data of the plurality of sub platforms to upper platforms.The object platform may be configured as the inspection robot in anintelligent production line. The control method may include: when theinspection robot inspects the detection site, obtaining detection dataof equipment or environment corresponding to the detection site,associating identification information of the detection site with thedetection data, packing the associated detection data into a detectionpackage, and sending the detection package to the sub platform of thesensor network platform corresponding to the detection site. Theidentification information of the detection site may at least comprisenumber information of detection site and number information of currentinspection robot. The sub platform of the sensor network platform mayreceive a detection packet, convert the detection packet into a datafile recognized by the management platform, and send data file to thesub platform of the management platform corresponding to the detectionsite. The sub platform of the management platform may receive the datafile, extract the detection data in the data file for comparison, obtaina comparison result, store identification information of the detectionsite, the detection data and the comparison result, and upload them to ageneral platform of the management platform. After receivingcorresponding data, based on the comparison result, the general platformof the management platform may execute: sending feedback instructions tocorresponding sub platform of the management platform by the generalplatform of the management platform, sending the feedback instructionsto the inspection robot through corresponding sub platform of the sensornetwork platform by the corresponding sub platform of the managementplatform, and continuing to perform detection after the inspection robotreceiving the feedback instructions; or the general platform of themanagement platform executes: sending the identification information ofthe detection site, the detection data, and the comparison result tocorresponding sub platform of the service platform the general platformof the management platform; receiving the corresponding data and sendingthe corresponding data to the user platform by the corresponding subplatform of the service platform, and sending processing instructionsbased on the corresponding data to the corresponding sub platform of theservice platform, the general platform of the management platform,corresponding sub platform of the management platform and correspondingsub platform of the sensor network platform by the user platform; andreceiving the processing instructions and converting the processinginstructions into an instruction file recognized by the inspection robotby the corresponding sub platform of the sensor network platform, andreceiving the instruction file and performing corresponding processingoperations by the inspection robot.

Those skilled in the art can realize that the units and algorithm stepsof the examples described in connection with the embodiments disclosedherein can be implemented in electronic hardware, computer software, ora combination of the two. In order to clearly illustrate theinterchangeability of hardware and software, the composition and stepsof the examples have been generally described in the above descriptionaccording to functions. Whether these functions are performed inhardware or software depends on the specific application and designconstraints of the technical scheme. Professional technicians can usedifferent methods to realize the described functions for each specificapplication, but such realization should not be considered beyond thescope of the present disclosure.

In the several embodiments provided in the present disclosure, it shouldbe understood that the disclosed devices and methods can be realized inother ways. For example, the device embodiments described above are onlyschematic. For example, the division of the unit is only a logicalfunction division. In actual implementation, there may be anotherdivision method, for example, multiple units or components may becombined or integrated into another system, or some features may beignored or not executed. In addition, the mutual coupling or directcoupling or communication connection shown or discussed may be indirectcoupling or communication connection through some interfaces, devices orunits, or may be electrical, mechanical or other forms of connection.

The units described as separate parts may or may not be physicallyseparated. As a person of ordinary skill in the art, it can be realizedthat the units and algorithm steps of the examples described incombination with the embodiments disclosed herein can be implemented inelectronic hardware, computer software, or a combination of the two. Inorder to clearly illustrate the interchangeability of hardware andsoftware, the composition and steps of the examples have been generallydescribed in the above description according to functions. Whether thesefunctions are performed in hardware or software depends on the specificapplication and design constraints of the technical scheme. Professionaltechnicians can use different methods to realize the described functionsfor each specific application, but such realization should not beconsidered beyond the scope of the present disclosure.

In addition, each functional unit in each embodiment of the presentdisclosure may be integrated into a processing unit, or each unit mayexist separately, or two or more units may be integrated into one unit.The above integrated units may be realized in the form of hardware orsoftware functional units.

The integrated unit may be stored in a computer readable storage mediumif it is realized in the form of a software functional unit and sold orused as an independent product. Based on this understanding, thetechnical solution of the present disclosure, in essence, or the partthat contributes to the prior art, or all or part of the technicalsolution, can be embodied in the form of a software product, which isstored in a storage medium, including a number of instructions to enablea computer device (which may be a personal computer, a server, or a griddevice, etc.) to perform all or part of the steps of the methodsdescribed in the various embodiments of the specification. Theaforementioned storage media may include: USB flash disk, mobile harddisk, read only memory (ROM), random access memory (RAM), magnetic discor optical disc and other media that can store program codes.

The specific embodiments described above have further described indetail the purpose, technical scheme and beneficial effects of thepresent disclosure. It should be understood that the above is only aspecific embodiment of the present disclosure and is not used to limitthe scope of protection of the present disclosure. Any modification,equivalent replacement, improvement, etc. made within the spirit andprinciples of this manual shall be included in the protection scope ofthis manual.

FIG. 4 shows an exemplary schematic diagram of determining a targetposition to be inspected based on a reinforcement learning modelaccording to some embodiments of the present disclosure. In someembodiments, the process 400 may be performed by the managementplatform.

As shown in FIG. 4 , the management platform may input environment stateinformation 410 into reinforcement learning model 420, and reinforcementlearning model 420 may output target position to be inspected 430according to the input environment state information 410. For moreinformation about the target position to be inspected, please refer toFIG. 1 and its related descriptions.

The environment state information may refer to information used todescribe states involved in process of the inspection robot performingthe inspection task. In some embodiments, the environment stateinformation 410 may include state information of the inspection robotand state information of the inspection task.

The state information of the inspection robot may refer to informationused to describe a current state of the inspection robot. For example,the state information of the inspection robot may include the currentposition of the inspection robot.

In some embodiments, the management platform may obtain the currentposition of the inspection robot through one or more position sensorsdeployed on the inspection robot or other means, so as to determine thestate information of the inspection robot.

State information of the inspection task may refer to information usedto describe a current state of the inspection task. For example, thestate information of the inspection task may include inspectioncondition of the inspection task. The inspection condition of theinspection task may include information on whether each detection sitein the inspection task is a position to be inspected and whetherdetection data of the detected position in the inspection task isnormal.

The inspection condition of the inspection task may be expressed invarious ways. In some embodiments, the inspection condition of theinspection task may be represented by an inspection condition vector.Dimension of the inspection condition vector may be a total number ofdetection sites set in the field environment. Each detection site in theinspection task may correspond to an element in the inspection conditionvector, and different element values may represent different meanings.For example, the element value may be defined as follows: an elementvalue of 0 may mean that the corresponding detection site is theposition to be detected; an element value of 1 may mean that thecorresponding detection site is the detected position and the detectiondata is normal; an element value of −1 may mean that the correspondingdetection site is the detected position and the detection data isabnormal; and an element value of −2 may mean that the inspection taskdoes not contain the detection site corresponding to the element. Forexample, it is assumed that the total number of detection sites set inthe field environment may be 10, including the No. 1 detection site, theNo. 2 detection site, . . . , and a No. 10 detection site. The detectiontask may be to detect the No. 3 detection site, a No. 5 detection site,and a No. 6 detection site and obtain detection data. The No. 3detection site may be the detected position and the detection data maybe normal, and the No. 5 detection site and the No. 6 detection site maybe the positions to be inspected. The inspection condition vector of theinspection task may be expressed as (−2, −2, 1, −2, 0, 0, −2, −2, −2,−2, −2).

In some embodiments, the inspection condition of the inspection task maybe stored in the management platform, and the management platform mayobtain the inspection condition of the inspection task to determine thestate information of the inspection task.

The reinforcement learning model 420 may be used to determine the targetposition to be inspected. The input of the reinforcement learning model420 may be environment state information 410, and the output of thereinforcement learning model 420 is the target position to be inspected430. The reinforcement learning model 420 may include an environmentmodule 421 and an optimal action determination module 422.

In some embodiments, when the target position to be inspected 430 isdetermined based on the reinforcement learning model 420, theenvironment state information 410 may be input into the reinforcementlearning model 420. In the model, the environment state information 410may be input into the environment module 421, and the environment module421 may output a set of optional actions. In the model, the environmentstate information 410 and the set of optional actions may be input intothe optimal action determination module 422, and the optimal actiondetermination module 422 may output an optimal optional action. Theposition to be inspected corresponding to the optimal optional action427 output by the optimal action determination module 422 may bedetermined as the target position to be inspected 430, which is used asoutput of the reinforcement learning model 420. For example, if theoptimal optional action is to go to the No. 1 detection position, thetarget position to be inspected may be the No. 1 detection site.

The environment module 421 may include an optional action determinationsub module 423, a state determination sub module 424, and a rewarddetermination sub module 425. In a prediction process of thereinforcement learning model 420, the environment module 421 maydetermine the set of optional actions through the optional actiondetermination sub module 423 based on the environment state information410. In a training process of reinforcement learning model 420, thestate determination sub module 424 and the reward determination submodule 425 in the environment module 421 may be used to determine theenvironment state information and the reward value at a next time,respectively.

The optional action determination sub module 423 may determine the setof optional actions of the inspection robot at the current time based onenvironmental state information of the current time.

The set of optional actions may refer to the set of actions that may beperformed by the inspection robot in an environmental state. The actionsthat may be performed by the inspection robot may be to go to a positionto be inspected in the inspection task for inspection. Under differentenvironmental conditions, a set of optional actions of the inspectionrobot may be different.

In some embodiments, the optional action determination sub module 423may detect the detection site corresponding to an element value of 0 inthe state information of the inspection task based on the stateinformation of the inspection task in the environment status informationat a current time, and determine it as the set of optional actions ofthe inspection robot at the current time. For example, the inspectiontask may be to detect the No. 1 detection site, the No. 2 detectionsite, and the No. 3 detection site and obtain the detection data. If thestate information of the inspection task at the current time is (0, 0,1), then “go to the No. 1 detection site and go to the No. 2 detectionsite” may be determined as set of optional actions of the inspectionrobot at the current time.

The state determination sub module 424 may determine the environmentstate information at the next time based on the environment stateinformation at the current time and the optimal optional action outputby the optimal action determination module. For example, the detectiontask may be to detect the No. 1 detection site, the No. 2 detectionsite, and the No. 3 detection site and obtain the detection data. Thestate information of the inspection task at the current time may be (0,0, 1). The optimal optional action output by the optimal actiondetermination module may be to go to the No. 1 detection site fordetection. After the inspection robot completes the detection and thedetection data is normal, the state determination sub module 424 maydetermine the state information of the inspection task at the next timeas (1, 0, 1).

The reward determination sub module 425 may be used to determine thereward value. The reward value may be used to evaluate an improvementdegree of inspection efficiency by actions performed by the inspectionrobot. For example, for actions with a high degree of improvement, thehigher the reward value may be; For actions with a low degree ofimprovement or negative improvement, the reward value may be lower. Thereward value may be expressed in numerical value or other ways. Theimprovement degree of inspection efficiency may be determined based onthe distance between the current position of the inspection robot andthe target position to be inspected. The closer the distance is, thesmaller a detour risk of the inspection robot is, the greater theimprovement degree of inspection efficiency is. In some embodiments, thereward determination sub module 425 may determine the reward value basedon a set formula.

In some embodiments, the reward value of the inspection robot performingan action may be related to the distance between the current position ofthe inspection robot and the target position to be inspectedcorresponding to the action. For example, reward values corresponding tovarious distance values may be recorded in preset comparison tables, andthen the reward values may be determined by looking up the presetcomparison table based on the distance values. The smaller the distancebetween the target position to be inspected corresponding to the actionand the current position of the inspection robot is, the greater thereward value of the inspection robot performing the action is.

In some embodiments, the reward value of the inspection robot performingan action may also be related to an abnormal probability of the positionto be inspected corresponding to the action. For example, the greaterthe abnormal probability of the position to be inspected correspondingto the action is, the greater the reward value of the inspection robotperforming the action is.

The abnormal probability may refer to a probability that the detectiondata of the position to be inspected is abnormal. Each abnormalprobability of each position to be inspected may be expressed in variousways, such as percentage.

In some embodiments, the abnormal probability of the position to beinspected may be determined based on historical detection conditions ofthe position to be inspected. The historical detection conditions of theposition to be inspected may include a number of times that the positionto be inspected has been historically performed and a number of timesthat historical detection data of the position to be inspected isabnormal. In some embodiments, the abnormal probability of the positionto be inspected may be a ratio of the number of times that thehistorical detection data of the position to be inspected is abnormal tothe number of times that the position to be inspected has beenhistorically performed.

In some embodiments, the management platform may also determine afrequent item related to the inspection condition vector of aninspection task based on a frequent item algorithm and determine theabnormal probability of the position to be inspected based on thefrequent item. For more information about determining the abnormalprobability of the position to be inspected based on the frequent item,please refer to FIG. 5 and its related descriptions.

In some embodiments, the management platform may also process theinspection condition vector of an inspection task based on an abnormalprobability determination model to determine an abnormal probabilityvector; determine the abnormal probability of the position to beinspected based on the abnormal probability vector. The abnormalprobability determination model may be a machine learning model. Formore information about determining the abnormal probability of theposition to be inspected based on the abnormal probability determinationmodel, please refer to FIG. 6 and its related descriptions.

In some embodiments, the management platform may determine the rewardvalue of the inspection robot performing an action based on a distancebetween the current position of the inspection robot and the position tobe inspected corresponding to the action, and the abnormal probabilityof the position to be inspected corresponding to the action. Forexample, the reward value of the inspection robot performing an actionmay be calculated by the following formula (1):

$\begin{matrix}{{r = {\frac{k_{1}\overset{\_}{d}}{d} + {k_{2}\frac{\overset{\_}{p}}{p}}}},} & (1)\end{matrix}$where r is the reward value of the action, d is the distance between thecurrent position of the inspection robot and the position to beinspected corresponding to the action, d is an average distance betweenthe current position of the inspection robot and the position to beinspected corresponding to the set of optional actions, p is theabnormal probability of the position to be inspected corresponding tothe action, p is an average value of abnormal probabilities of theposition to be inspected corresponding to the set of optional actions,k₁ and k₂ are preset parameters for adjusting a base size of the rewardvalue. The greater the k₁ is, the greater the reward value brought bychoosing a better distance is. The inspection robot may be more inclinedto choose a closer position to be inspected as the target position to beinspected. The greater the k₂ is, the greater the reward value broughtby choosing a better abnormal probability is. The inspection robot maybe more inclined to choose a location to be inspected with a higherabnormal probability as the target position to be inspected. k₁ and k₂may be determined based on experience. For example, k₁ and k₂ may bothbe 1.

In some embodiments of the present disclosure, the target position to beinspected may be determined by introducing the abnormal probability ofthe position to be inspected based on the distance between the currentposition of the inspection robot, and the position to be inspected, aswell as the abnormal probability of the position to be inspected. Thismethod makes the inspection robot not only consider the distance, butalso consider the position to be inspected with high abnormalprobability under a decision-making of the reinforcement learning model(such inspection robots need to go to the inspection position first toeliminate the fault as soon as possible). Therefore, factors consideredby the inspection robot when carrying out inspection are morecomprehensive.

The optimal action determination module 422 may determine the optimaloptional action based on the environmental state information 410 and theset of optional actions at the current time. An input of the optimalaction determination module 422 may be the environment state information410 and the set of optional actions. An output of the optimal actiondetermination module 422 may be the optimal optional action 427.

In some embodiments, for each optional action in the set of optionalactions, the optimal action determination module 422 may internallyoutput a recommended value, and the optimal action determination modulemay determine the optional action with a largest recommended value asthe optimal optional action and output it.

In some embodiments, the optimal action determination module 422 may bea machine learning model and may be implemented by various methods, suchas deep neural network (DNN), convolutional neural network (CNN), cyclicneural network (RNN), etc.

In some embodiments, the optimal action determination module may betrained based on reinforcement learning methods, such as deep Q-learningnetwork (DQN), double deep Q-learning network (DDQN), etc. Trainingsamples may be historical environment state information, and a label maybe a corresponding optimal optional action under the historicalenvironment state information. The training samples may be obtainedbased on historical data. Labels of the training samples may be obtainedby reinforcement learning.

In some embodiments, the management platform may periodically performthe reinforcement learning model 420 and output the optimal optionalactions based on a preset trigger condition. For example, the presettrigger condition may be that the inspection robot completes the optimaloptional action currently output by the reinforcement learning model420.

In some embodiments of the present disclosure, by defining the rewardvalue related to a distance between the current position and theposition to be inspected, the reinforcement learning model may betrained based on the reward value under the definition, so that atrained reinforcement learning model may select the target position tobe inspected at a current time from a global optimization, rather thanonly selecting the target position to be inspected based on a currentoptimization. Therefore, an inspection efficiency of the inspectionrobot may be effectively improved.

FIG. 5 shows an exemplary flowchart for determining an abnormalprobability of the target position to be inspected based on a frequentitem according to some embodiments of the present disclosure. In someembodiments, the process 500 may be executed by the management platform.As shown in FIG. 5 , the process 500 may include the following steps.

In step 510, determining a frequent item based on a historical detectioncondition set.

The historical detection condition set may refer to a set composed ofthe detection conditions of historical detection tasks. For example, thehistorical detection condition set may be a set of inspection conditionvectors of at least one historical detection task. In some embodiments,the management platform may determine the historical detectionconditions set by obtaining detection conditions of at least one storedhistorical detection task.

The frequent item may refer to an item set whose support meets presetconditions. The item set may refer to a set composed of at least onedata item. For example, k item set may refer to a set composed of k dataitems. The data items may refer to detection conditions of the detectionsites. For example, 3 item set may be: detection data of the No. 1detection site is normal, detection data of the No. 3 detection site isnormal, and detection data of a No. 7 detection site is abnormal.

Support may refer to a frequency of appearance of an item set in thehistorical detection condition set. For example, if the support of anitem set is 3, it may mean that the item set has appeared 3 times in thehistorical detection condition set.

In some embodiments, a threshold may be set and an item set with asupport greater than the threshold and a maximum k value may bedetermined as the frequent item. For example, if there are 1 item set, 2item set, and 3 item set that meet support conditions, and there are no4 item set that meet the support conditions, the 3 item set that meetthe support conditions may be determined as the frequent item. In someembodiments, the frequent item may be determined using algorithms suchas FP growth, Apriori, etc.

In step 520, determining a target frequent item based on a degree ofconformity between the frequent item and the current inspection task.

The degree of conformity may refer to a degree to which the detectionsites and their detection conditions in the frequent item are consistentwith the detected positions and their detection conditions in thecurrent inspection task. For example, the degree of conformity may bedetermined by the following formula (2):a=b ₁ b ₂  (2),where a is the degree of conformity, b₁ is a proportion of detectionsites in the frequent item contained in the detected positions in thecurrent inspection task, b₂ is a conformity ratio between detectioncondition of the detection sites in the frequent item contained in thedetected positions in the current inspection task and detectioncondition of the detection sites in the frequent item. For example, afrequent item may be that: detection data of the No. 1 detection site isnormal, detection data of the No. 3 detection site is normal, detectiondata of the No. 5 detection site is abnormal, and detection data of theNo. 6 detection site is abnormal. The detected positions in theinspection task and their detection conditions may be that: detectiondata of the No. 3 detection site is normal, detection data of the No. 5detection site is normal, detection data of a No. 8 detection site isnormal. Then, the detected positions in the inspection task are the No.3 detection site, the No. 5 detection site and the No. 8 detection site,which include the No. 3 detection site and the No. 5 detection site inthe frequent item, so there is b₁=2/3. In the above two detection sitesof the No. 3 detection site and the No. 5 detection site, only thedetection data of the No. 3 detection site is the same (both arenormal), so there is b₂=1/2.

In some embodiments, the management platform may determine one or morefrequent items whose degree of conformity is greater than the thresholdand including the position to be inspected where the abnormalprobability is currently to be determined as one or more target frequentitems. For example, if a threshold of the degree of conformity is set to80%, in the current inspection task, the detected positions and theirdetection conditions may be that: the No. 1 detection position isnormal, the No. 2 detection position is normal, and the No. 3 detectionposition is normal. The positions to be inspected may include the No. 4detection position and the No. 5 detection position. Currently, theposition to be inspected to determine the abnormal probability may beset to No. 4 detection position. Frequent item A may be that: if the No.1 detection site is normal, the No. 2 detection site is normal, the No.3 detection site is normal, and the No. 4 detection site is normal, thenthe degree of conformity of the frequent item A may be: 3/3=100%, whichis greater than 80% of the threshold of the degree of conformity, andthe frequent item may include the No. 4 detection site (i.e. theposition to be inspected where the abnormal probability is currently tobe determined). Therefore, the frequent item A may be the targetfrequent item corresponding to the position to be inspected where theabnormal probability is currently to be determined.

In step 530, determining positive target frequent items and negativetarget frequent items based on the one or more target frequent items.

The positive target frequent items may refer to target frequent items inwhich detection data of detection sites is abnormal corresponding to theposition to be inspected where the abnormal probability is currently tobe determined in the inspection task.

The negative target frequent items may refer to target frequent items inwhich detection data of detection sites is normal corresponding to theposition to be inspected where the abnormal probability is currently tobe determined in the inspection task.

For example, the frequent item A is set as that: detection data of theNo. 1 detection site is normal, detection data of the No. 3 detectionsite is normal, detection data of the No. 5 detection site is abnormal,and detection data of the No. 6 detection site is abnormal; a frequentitem B is set as that: detection data of the No. 1 detection site isnormal, detection data of the No. 3 detection site is normal, detectiondata of the No. 5 detection site is abnormal, and detection data of theNo. 6 detection site is normal. If the position to be inspected wherethe abnormal probability is currently to be determined in the inspectiontask is the No. 6 detection site, then the frequent item A may be apositive target frequent item, and the frequent item B may be a negativetarget frequent item.

In step 540, determining a probability adjustment factor based on thepositive target frequency items and the negative target frequency items.

The probability adjustment factor may be used to adjust the abnormalprobability of the position to be inspected. The probability adjustmentfactor may be expressed in various ways. For example, the probabilityadjustment factor may be a real number greater than 0.

In some embodiments, the management platform may determine theprobability adjustment factor based on a ratio of a number of positivetarget frequent items to a number of negative target frequent items. Forexample, the probability adjustment factor may be calculated by thefollowing formula (3):c=n ₁ /n ₂  (3),Where c is the probability adjustment factor, n₁ is a number of thepositive target frequency items, n₂ is a number of the negative targetfrequency items.

In step 550, determining the abnormal probability of the position to beinspected based on the probability adjustment factor.

In some embodiments, the management platform may determine a product ofan initial abnormal probability of the position to be inspected and theprobability adjustment factor as the abnormal probability of theposition to be inspected. The initial abnormal probability of theposition to be inspected may be determined based on the historicaldetection conditions of the position to be inspected. For example, theinitial abnormal probability of the position to be inspected may be theratio of the number of times that the historical detection data of theposition to be inspected may be abnormal to the number of times that theposition to be inspected has been historically performed.

For example, the abnormal probability of the position to be inspectedmay be calculated by the following formula (4):p _(n) =cp _(o)  (4),where p_(n) is the abnormal probability of the position to be inspected,c is the probability adjustment factor, p_(o) is the initial abnormalprobability of the position to be inspected.

In some embodiments, the management platform may update the abnormalprobability of the position to be inspected based on newly obtaineddetection data. For example, operations of determining the abnormalprobability of the position to be inspected based on the frequent itemmay be performed periodically based on preset trigger conditions. Thepreset trigger conditions may be that the inspection robot goes to aposition to be inspected in the inspection task for inspection.

In some embodiments of the present disclosure, by determining thefrequent item, the abnormal probability of the position to be inspectedmay be adjusted based on the detection data of the detected position,reflecting an internal correlation between the detection sites, and theabnormal probability of the position to be inspected may be moreaccurately determined.

FIG. 6 shows an exemplary schematic diagram of determining the abnormalprobability of the target position to be inspected based on the abnormalprobability determination model according to some embodiments of thepresent disclosure. In some embodiment, the process 600 may be performedby the management platform.

In some embodiments, the management platform may process the inspectioncondition vector 610 of the inspection task to determine the abnormalprobability vector 630 based on the abnormal probability determinationmodel 620, and determine the abnormal probability of the position to beinspected based on the abnormal probability vector 630. The abnormalprobability determination model 620 may be a machine learning model.

The abnormal probability vector may refer to a vector composed of theabnormal probability of the position to be inspected in the inspectiontask. Dimensions of the abnormal probability vector may be a totalnumber of detection sites set in the field environment. Each element inthe abnormal probability vector may correspond to a detection site, anddifferent element values may represent different meanings.

For example, the element values may be defined as follows: an elementvalue of −1 may mean that a corresponding detection site is the detectedposition and corresponding detection data is normal; an element value of−2 may mean that a corresponding detection site is the detected positionand corresponding detection data is abnormal; a positive element valuemay mean that a corresponding detection site is the position to beinspected and the positive element value may be the abnormal probabilityof the position to be inspected output by the abnormal probabilitydetermination model; and an element value of −3 may mean that theinspection task does not contain a detection site corresponding to theelement. For example, the total number of detection sites set in thefield environment is 10, which are the No. 1 detection site, the No. 2detection site, . . . , and the No. 10 detection site in turn. Theinspection task may be to detect the No. 3 detection site, the No. 5detection site, and the No. 6 detection site and obtain correspondingdetection data. The No. 3 detection position may be the detectedposition and corresponding detection data may be normal, and the No. 5detection position and the No. 6 detection position may be the positionsto be inspected. Then an abnormal probability vector output by theabnormal probability determination model may be expressed as (−3, −3,−1, −3, 35, 25, −3, −3, −3, −3, and −3). From this vector, it can beknown abnormal probabilities of the positions to be inspected may bethat: an abnormal probability of the No. 5 detection site may be 35% andan abnormal probability of the No. 6 detection site may be 25%.

In some embodiments, the management platform may input the inspectioncondition vector 610 of the inspection task into the abnormalprobability determination model 620, and determine the abnormalprobability vector 630 through the abnormal probability determinationmodel 620.

In some embodiments, the management platform may obtain elements whoseelement value is a positive number in the abnormal probability vector630, and successively determine each element value as the abnormalprobability of the position to be inspected corresponding to eachelement.

In some embodiments, determining the abnormal probability of theposition to be inspected based on the abnormal probability determinationmodel may be carried out periodically based on the preset triggercondition. For example, the preset trigger condition may be that theinspection robot goes to a position to be inspected in the inspectiontask for inspection.

In some embodiments, the abnormal probability determination model 620may be obtained by training. For example, the training samples may beinput into an initial abnormal probability determination model 621, aloss function may be constructed based on outputs of the initialabnormal probability determination model 621, and parameters of theinitial abnormal probability determination model 621 may be iterativelyupdated based on the loss function until the preset trigger condition ismet and the training is completed.

In some embodiments, the training samples may include the inspectioncondition vector 611 of historical inspection tasks. The trainingsamples may be obtained based on historical data, and the label may bethe abnormal probability vector 631 composed of an abnormal probabilityof remaining positions to be inspected under a detection condition ofthe historical inspection tasks. The label may be marked based on theabnormal probability of remaining positions to be inspected under ahistorical detection condition of the manual statistical historicaldetection inspections.

In some embodiments of the present disclosure, the abnormal probabilitydetermination model 1250 may be trained based on a large amount ofhistorical data, so that the model can effectively learn an internalrelationship between the inspection condition vector of the inspectiontask and the abnormal probability of the position to be inspected, sothat the abnormal probability of the position to be inspected can beaccurately predicted.

The basic concepts have been described above. Obviously, for thoseskilled in the art, the above detailed disclosure is only an example anddoes not constitute a limitation of the present disclosure. Although itis not explicitly stated here, those skilled in the art may make variousmodifications, improvements and amendments to the present disclosure.Such modifications, improvements and amendments are suggested in thepresent disclosure, so such modifications, improvements and amendmentsstill belong to the spirit and scope of the exemplary embodiments of thepresent disclosure.

Meanwhile, the present disclosure uses specific words to describe theembodiments of the present disclosure. For example, “one embodiment”,and/or “some embodiments” mean a certain feature or structure related toat least one embodiment of the present disclosure. Therefore, it shouldbe emphasized and noted that “one embodiment” or “an alternativeembodiment” mentioned twice or more in different positions in thepresent disclosure does not necessarily refer to the same embodiment. Inaddition, certain features or structures in one or more embodiments ofthe present disclosure may be appropriately combined.

In addition, unless explicitly stated in the claims, the sequence ofprocessing elements and sequences, the use of numbers and letters, orthe use of other names described in the present disclosure are not usedto define the sequence of processes and methods in the presentdisclosure. Although the above disclosure has discussed some currentlyconsidered useful embodiments of the invention through various examples,it should be understood that such details are only for the purpose ofexplanation, and the additional claims are not limited to the disclosedembodiments. On the contrary, the claims are intended to cover allamendments and equivalent combinations that conform to the essence andscope of the embodiments of the present disclosure. For example,although the system components described above can be implemented byhardware devices, they can also be implemented only by softwaresolutions, such as installing the described system on an existing serveror mobile device.

Similarly, it should be noted that, in order to simplify the descriptiondisclosed in the present disclosure and thus help the understanding ofone or more embodiments of the invention, the foregoing description ofthe embodiments of the present disclosure sometimes incorporates avariety of features into one embodiment, the drawings or the descriptionthereof. However, this disclosure method does not mean that the objectof the present disclosure requires more features than those mentioned inthe claims. In fact, the features of the embodiments are less than allthe features of the single embodiments disclosed above.

In some embodiments, numbers describing the number of components andattributes are used. It should be understood that such numbers used inthe description of embodiments are modified by the modifier “about”,“approximate” or “generally” in some examples. Unless otherwise stated,“approximately” or “generally” indicate that a ±20% change in the figureis allowed. Accordingly, in some embodiments, the numerical parametersused in the description and claims are approximate values, and theapproximate values can be changed according to the characteristicsrequired by individual embodiments. In some embodiments, the numericalparameter should consider the specified significant digits and adopt themethod of general digit reservation. Although the numerical fields andparameters used to confirm the range breadth in some embodiments of thepresent disclosure are approximate values, in specific embodiments, thesetting of such values is as accurate as possible within the feasiblerange.

For each patent, patent application, patent application disclosure andother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, etc., the entirecontents are hereby incorporated into the present disclosure forreference. Except for the application history documents that areinconsistent with or conflict with the contents of the presentdisclosure, and the documents that limit the widest range of claims inthe present disclosure (currently or later appended to the presentdisclosure). It should be noted that in case of any inconsistency orconflict between the description, definitions, and/or use of terms inthe supplementary materials of the present disclosure and the contentsdescribed in the present disclosure, the description, definitions,and/or use of terms in the present disclosure shall prevail.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other deformations may also fallwithin the scope of the present disclosure. Therefore, as an examplerather than a limitation, the alternative configuration of theembodiments of the present disclosure can be regarded as consistent withthe teachings of the present disclosure. Accordingly, the embodiments ofthe present disclosure are not limited to those explicitly introducedand described in the present disclosure.

What is claimed is:
 1. An Industrial Internet of Things system forinspection data processing, comprising a user platform, a serviceplatform, a management platform, a sensor network platform, and anobject platform interacting sequentially from top to bottom, wherein theservice platform and the sensor network platform adopt an independentlayout, and the management platform adopts a front sub platform layout;the independent layout means that the service platform or the sensornetwork platform is provided with a plurality of independent subplatforms, the plurality of independent sub platforms respectivelystore, process, and/or transmit data of different lower platforms; thefront sub platform layout means that the management platform is providedwith a general platform and a plurality of sub platforms, the pluralityof sub platforms respectively store and process data of different typesor different receiving objects sent by different lower platforms, andthe general platform stores and processes data of the plurality of subplatforms after summarizing, and transmits the data of the plurality ofsub platforms to upper platforms; the object platform is configured asan inspection robot in an intelligent production line; when theinspection robot inspects a detection site, the inspection robot is usedto obtain detection data of equipment or environment corresponding tothe detection site, associate identification information of thedetection site with the detection data, pack the associated detectiondata into a detection package, and send the detection package to a subplatform of the sensor network platform corresponding to the detectionsite; the corresponding sub platform of the sensor network platform isconfigured to receive the detection package, convert the detectionpackage into a data file recognized by the management platform, and sendthe data file to a sub platform of the management platform correspondingto the detection site; the corresponding sub platform of the sensornetwork platform is further configured to receive processinginstructions sent by the user platform through a corresponding subplatform of the service platform, the general platform of the managementplatform, and the corresponding sub platform of the management platform,and convert the processing instructions into an instruction filerecognized by the inspection robot, and the inspection robot receivesthe instruction file and performs corresponding move and detectionoperations; the corresponding sub platform of the management platform isconfigured to receive the data file, extract the detection data in thedata file for comparison, obtain a comparison result, storeidentification information of the detection site, the detection data,and the comparison result, and upload the identification information ofthe detection site, the detection data, and the comparison result as acorresponding data of the sub platform of the management platform to thegeneral platform of the management platform; after receiving thecorresponding data of the sub platform of the management platform, basedon the comparison result, the general platform of the managementplatform is configured to send feedback instructions to thecorresponding sub platform of the management platform, the correspondingsub platform of the management platform sending the feedbackinstructions to the inspection robot through the corresponding subplatform of the sensor network platform, and the inspection robotcontinuing to perform detection after receiving the feedbackinstructions before moving to a next detection site; or the generalplatform of the management platform is configured to send theidentification information of the detection site, the detection data,and the comparison result to the corresponding sub platform of theservice platform, the corresponding sub platform of the service platformreceiving the identification information of the detection site, thedetection data, and the comparison result and sending the identificationinformation of the detection site, the detection data, and thecomparison result to the user platform, and the user platform sending,based on the identification information of the detection site, thedetection data, and the comparison result, the processing instructionsto the corresponding sub platform of the service platform, the generalplatform of the management platform, the corresponding sub platform ofthe management platform, and the corresponding sub platform of thesensor network platform; wherein the general platform of the managementplatform is further configured to: when the inspection robot performsinspection and the user platform sends detection instructions of aspecified detection site, obtain latest detection site information ofthe inspection robot based on the stored identification information ofthe detection site, take next two detection sites of a detection sitecorresponding to the detection site information as target objects, andsend the detection instruction of the specified detection site to subplatforms of the sensor network platform corresponding to the targetobjects; and when the inspection robot inspects any one of the targetobjects, receive the detection instruction of specified detection site,interrupt the detection, move to the specified detection site fordetection, and return to the interrupted detection site to continue thedetection after the detection for the specified detection site iscompleted.
 2. The Industrial Internet of Things system for inspectiondata processing of claim 1, wherein when the inspection robot inspectsany one of the target objects, the inspection robot is furtherconfigured to receive the detection instruction of the specifieddetection site and simultaneously send instruction receiving informationto the corresponding sub platform of the sensor network platform; andthe sub platform of the sensor network platform sends the instructionreceiving information to the general platform of the management platformthrough the corresponding sub platform of the management platform; thegeneral platform of the management platform is further configured toobtain the instruction receiving information and send instructioncancellation information to a sub platform of the management platformcorresponding to another target object; and the sub platform of themanagement platform corresponding to the another target object isfurther configured to receive the instruction cancellation informationand send the instruction cancellation information to the sub platform ofthe sensor network platform corresponding to the another target object,after the sub platform of the sensor network platform corresponding tothe another target object receiving the instruction cancellationinformation, the sub platform of the sensor network platformcorresponding to the another target object cancels the detectioninstruction of the specified detection site.
 3. The Industrial Internetof Things system for inspection data processing of claim 1, wherein thecorresponding sub platform of the management platform is furtherconfigured to: pre-store a detection data comparison table correspondingto a detection site in the corresponding sub platform of the managementplatform; extract the detection data in the data file and compare thedetection data with data in the detection data comparison table afterreceiving the data file; the comparison result being that the data isnormal when the detection data meets data requirements in the detectiondata comparison table; and the comparison result being that the data isabnormal when the detection data does not meet the data requirements inthe detection data comparison table; and correlate the identificationinformation of the detection site, the detection data, and thecomparison result, store the identification information of the detectionsite, the detection data, and the comparison result and upload theidentification information of the detection site, the detection data,and the comparison result to the general platform of the managementplatform after the comparison is completed.
 4. The Industrial Internetof Things system for inspection data processing of claim 3, wherein inresponse to the comparison result is that the data is normal, thegeneral platform of the management platform sends the feedbackinstructions, and the inspection robot continues to perform detectionafter receiving the feedback instructions and moves to a next detectionsite.
 5. The Industrial Internet of Things system for inspection dataprocessing of claim 3, wherein in response to the comparison result isthat the data is abnormal, the general platform of the managementplatform is further configured to send the identification information ofthe detection site, the detection data, and the comparison result as acorresponding data of the sub platform of the service platform to thecorresponding sub platform of the service platform; the correspondingsub platform of the service platform receives the corresponding data ofthe sub platform of the service platform and sends the correspondingdata of the sub platform of the service platform to the user platform,the user platform is further configured to send the processinginstructions based on the corresponding data of the sub platform of theservice platform, the processing instructions at least including thedetection site information and inspection task adjustment data; the userplatform sends the processing instructions to the corresponding subplatform of the service platform, the general platform of the managementplatform, the corresponding sub platform of the management platform, andthe corresponding sub platform of the sensor network platform; thecorresponding sub platform of the sensor network platform receives theprocessing instructions and converts the processing instructions intothe instruction file recognized by the inspection robot; and theinspection robot is further configured to receive the instruction file,extract the inspection task adjustment data in the instruction file, andperform inspection based on the inspection task adjustment data.
 6. TheIndustrial Internet of Things system for inspection data processing ofclaim 5, wherein the inspection robot is further configured to: receivethe instruction file, and extract the inspection task adjustment data inthe instruction file; and take the inspection task adjustment data asupdate data to update inspection data originally stored by theinspection robot, and after the update, perform inspection by theinspection robot according to the updated inspection data, wherein theinspection data at least includes an inspection route, coordinates ofthe detection site, and detection items of the detection site.
 7. TheIndustrial Internet of Things system for inspection data processing ofclaim 1, wherein the identification information of the detection site atleast includes number information of the detection site and numberinformation of the current inspection robot.
 8. A control method of anIndustrial Internet of Things for inspection data processing, theIndustrial Internet of Things for inspection data processing comprisinga user platform, a service platform, a management platform, a sensornetwork platform, and an object platform interacted sequentially fromtop to bottom, wherein the service platform and the sensor networkplatform adopt an independent layout, and the management platform adoptsa front sub platform layout; the independent layout means that theservice platform or the sensor network platform is provided with aplurality of independent sub platforms, the plurality of independent subplatforms respectively store, process, and/or transmit data of differentlower platforms; the front sub platform layout means that the managementplatform is provided with a general platform and a plurality of subplatforms, the plurality of sub platforms respectively store and processthe data of different types or different receiving objects sent by thedifferent lower platforms, and the general platform stores and processesdata of the plurality of sub platforms after summarizing, and transmitsthe data of the plurality of sub platforms to upper platforms; theobject platform is configured as an inspection robot in an intelligentproduction line; the control method comprises: when the inspection robotinspects a detection site, obtaining detection data of equipment orenvironment corresponding to the detection site, associatingidentification information of the detection site with the detectiondata, packing the associated detection data into a detection package,and sending the detection package to a sub platform of the sensornetwork platform corresponding to the detection site; and receiving, bythe corresponding sub platform of the sensor network platform, thedetection package, converting the detection package into a data filerecognized by the management platform, and sending the data file to asub platform of the management platform corresponding to the detectionsite; receiving, by the corresponding sub platform of the managementplatform, the data file, extracting the detection data in the data filefor comparison, obtaining a comparison result, storing identificationinformation of the detection site, the detection data, and thecomparison result, and uploading the identification information of thedetection site, the detection data, and the comparison result as acorresponding data of the sub platform of the management platform to thegeneral platform of the management platform; after receiving thecorresponding data of the sub platform of the management platform, basedon the comparison result, sending, by the general platform of themanagement platform, feedback instructions to corresponding sub platformof the management platform, sending, by the corresponding sub platformof the management platform, the feedback instructions to the inspectionrobot through the corresponding sub platform of the sensor networkplatform, and the inspection robot continuing to perform detection afterreceiving the feedback instructions before moving to a next detectionsite; or sending, by the general platform of the management platform,the identification information of the detection site, the detectiondata, and the comparison result to the corresponding sub platform of theservice platform; receiving, by the corresponding sub platform of theservice platform, the identification information of the detection site,the detection data, and the comparison result and sending theidentification information of the detection site, the detection data,and the comparison result to the user platform, and sending, by the userplatform, based on the identification information of the detection site,the detection data, and the comparison result, processing instructionsto the corresponding sub platform of the service platform, the generalplatform of the management platform, the corresponding sub platform ofthe management platform, and the corresponding sub platform of thesensor network platform; and receiving, by the corresponding subplatform of the sensor network platform, the processing instructions andconverting the processing instructions into an instruction filerecognized by the inspection robot, and the inspection robot receivingthe instruction file and performing a corresponding move and detectionoperations, wherein the method further comprises: when the inspectionrobot performs inspection and the user platform sends detectioninstructions of a specified detection site, obtaining, by the generalplatform of the management platform, latest detection site informationof the inspection robot based on the stored identification informationof the detection site, taking next two detection sites of a detectionsite corresponding to the detection site information as target objects,and sending the detection instruction of the specified detection site tosub platforms of the sensor network platform corresponding to the targetobjects; and when the inspection robot inspects any one of the targetobjects, receiving, by the general platform of the management platform,the detection instruction of specified detection site, interrupting thedetection, moving to the specified detection site for detection, andreturning to the interrupted detection site to continue the detectionafter the detection for the specified detection site is completed.